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A collective approach to reach known and unknown target in multi agent environment using nature inspired algorithms

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Abstract

Robotics is a vast and growing area in academia and industry, and it is used to solve human problems through artificial intelligent machines. To solve crucial issues or problems such as the survivability of humans in mining, living in Antarctica, Space, mountainous regions, and other challenging terrains, we need an intelligent machine. Working with multiple robots is more beneficial than with a single robot. This article proposed a method to search and track the target in an unknown environment through a multi-agent system using a nature-inspired algorithm (NIA). Here, the location of the target is either known or unknown. To reach the target, mobile agents use two types of movement, either directional-based movement or nature-inspired algorithm-based movement. Directional-based movements help the mobile agents decide the point of interest to move. The nature-inspired algorithm (NIA) based movement helps the agents explore the area in the provided direction. This work uses particle swarm optimization (PSO), bacteria foraging optimization (BFO), and bat algorithm (BA) NIA for the exploration of the area. The proposed method has been tested in simple and complex environments. From the experimental analysis, the proposed method successfully tracks or searches the target in an unknown environment. The experiments show that the agent movement behavior is affected by some factors such as the complexity of the environment, number of agents, sensor range, etc. The results indicate that the bat algorithm outperforms the other two algorithms in terms of fast time completion.

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  1. Computer Science and Engineering Department, Indian Institute of Information Technology Pune, Maharshtra, 411041, India

    Sanjeev Sharma & Mahendra Pratap Yadav

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  1. Sanjeev Sharma
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Correspondence to Mahendra Pratap Yadav.

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Sharma, S., Yadav, M.P. A collective approach to reach known and unknown target in multi agent environment using nature inspired algorithms. Cluster Comput 27, 11369–11392 (2024). https://doi.org/10.1007/s10586-024-04523-2

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